Xiao Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Xiao Yang has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
Marc Niethammer
University of North Carolina at Chapel Hill, Chapel Hill, USA; Biomedical Research Imaging Center (BRIC), Chapel Hill, USA
3 shared publicationsRoland Kwitt
Department of Computer Science, University of Salzburg, Austria
3 shared publicationsEunbyung Park
UNC Chapel Hill, Chapel Hill, USA
1 shared publication- Greg Fleishman
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
1 shared publication - Marc Niethammer
University of North Carolina at Chapel Hill
1 shared publication Martin Styner
University of North Carolina at Chapel Hill, Chapel Hill, USA; Department of Psychiatry, UNC, Chapel Hill, USA
1 shared publication
Latest Publications
- 1 August 2019
- Published by Elsevier in Medical Image Analysis
- Vol. 56, 193-209
Fast predictive simple geodesic regression
- 1 August 2019
- Published by Elsevier in Medical Image Analysis
- Vol. 56, 193-209
Fast predictive simple geodesic regression
- Top Cited Papers
- 1 September 2017
- Published by Elsevier in NeuroImage
- Vol. 158, 378-396
Quicksilver: Fast predictive image registration – A deep learning approach
- 1 September 2017
- Published by Elsevier in NeuroImage
- Vol. 158, 378-396
Top Cited PapersQuicksilver: Fast predictive image registration – A deep learning approach
- 23 September 2016
- Published by Springer Nature in Lecture Notes in Computer Science
- Vol. 9968, 97-107
Registration of Pathological Images
This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, for which more accurate registration is possible. Specifically, the method uses a deep variational convolutional encoder-decoder network to learn the mapping. Furthermore, the method estimates local mapping uncertainty through network inference statistics and uses those estimates to down-weight the image registration similarity measure in areas of high uncertainty. The performance of the method is quantified using synthetic brain tumor images and images from the brain tumor segmentation challenge (BRATS 2015).
- 23 September 2016
- Published by Springer Nature in Lecture Notes in Computer Science
- Vol. 9968, 97-107
Registration of Pathological Images
This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, for which more accurate registration is possible. Specifically, the method uses a deep variational convolutional encoder-decoder network to learn the mapping. Furthermore, the method estimates local mapping uncertainty through network inference statistics and uses those estimates to down-weight the image registration similarity measure in areas of high uncertainty. The performance of the method is quantified using synthetic brain tumor images and images from the brain tumor segmentation challenge (BRATS 2015).